{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**House Prices**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "  function code_toggle() {\n",
       "    if (code_shown){\n",
       "      $('div.input').hide('500');\n",
       "      $('#toggleButton').val('Show Code')\n",
       "    } else {\n",
       "      $('div.input').show('500');\n",
       "      $('#toggleButton').val('Hide Code')\n",
       "    }\n",
       "    code_shown = !code_shown\n",
       "  }\n",
       "\n",
       "  $( document ).ready(function(){\n",
       "    code_shown=true;\n",
       "  });\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Hide Code\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('''\n",
    "<script>\n",
    "  function code_toggle() {\n",
    "    if (code_shown){\n",
    "      $('div.input').hide('500');\n",
    "      $('#toggleButton').val('Show Code')\n",
    "    } else {\n",
    "      $('div.input').show('500');\n",
    "      $('#toggleButton').val('Hide Code')\n",
    "    }\n",
    "    code_shown = !code_shown\n",
    "  }\n",
    "\n",
    "  $( document ).ready(function(){\n",
    "    code_shown=true;\n",
    "  });\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Hide Code\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['train.csv', 'sample_submission.csv', 'test.csv', 'data_description.txt']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>...</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning    ...     SaleType  SaleCondition SalePrice\n",
       "0   1          60       RL    ...           WD         Normal    208500\n",
       "1   2          20       RL    ...           WD         Normal    181500\n",
       "2   3          60       RL    ...           WD         Normal    223500\n",
       "3   4          70       RL    ...           WD        Abnorml    140000\n",
       "4   5          60       RL    ...           WD         Normal    250000\n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"../input/train.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
      "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
      "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
      "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
      "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
      "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
      "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
      "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
      "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
      "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
      "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
      "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
      "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
      "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
      "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
      "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
      "       'SaleCondition', 'SalePrice'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>Id</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>MSSubClass</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>MSZoning</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>17.739726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>Street</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>Alley</td>\n",
       "      <td>93.767123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape</th>\n",
       "      <td>LotShape</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour</th>\n",
       "      <td>LandContour</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig</th>\n",
       "      <td>LotConfig</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>LandSlope</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1</th>\n",
       "      <td>Condition1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2</th>\n",
       "      <td>Condition2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <td>BldgType</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle</th>\n",
       "      <td>HouseStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>YearRemodAdd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>RoofStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>RoofMatl</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>Exterior1st</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>Exterior2nd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>MasVnrType</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>ExterQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>ExterCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>Foundation</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>KitchenAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>KitchenQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>TotRmsAbvGrd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>Functional</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>Fireplaces</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>FireplaceQu</td>\n",
       "      <td>47.260274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>GarageType</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>GarageYrBlt</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>GarageFinish</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>GarageArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>GarageQual</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>GarageCond</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive</th>\n",
       "      <td>PavedDrive</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>EnclosedPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>ScreenPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>PoolArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>PoolQC</td>\n",
       "      <td>99.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>Fence</td>\n",
       "      <td>80.753425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>MiscFeature</td>\n",
       "      <td>96.301370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>MiscVal</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>MoSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>YrSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>SaleType</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>SaleCondition</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>SalePrice</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 column_name  percent_missing\n",
       "Id                        Id         0.000000\n",
       "MSSubClass        MSSubClass         0.000000\n",
       "MSZoning            MSZoning         0.000000\n",
       "LotFrontage      LotFrontage        17.739726\n",
       "LotArea              LotArea         0.000000\n",
       "Street                Street         0.000000\n",
       "Alley                  Alley        93.767123\n",
       "LotShape            LotShape         0.000000\n",
       "LandContour      LandContour         0.000000\n",
       "Utilities          Utilities         0.000000\n",
       "LotConfig          LotConfig         0.000000\n",
       "LandSlope          LandSlope         0.000000\n",
       "Neighborhood    Neighborhood         0.000000\n",
       "Condition1        Condition1         0.000000\n",
       "Condition2        Condition2         0.000000\n",
       "BldgType            BldgType         0.000000\n",
       "HouseStyle        HouseStyle         0.000000\n",
       "OverallQual      OverallQual         0.000000\n",
       "OverallCond      OverallCond         0.000000\n",
       "YearBuilt          YearBuilt         0.000000\n",
       "YearRemodAdd    YearRemodAdd         0.000000\n",
       "RoofStyle          RoofStyle         0.000000\n",
       "RoofMatl            RoofMatl         0.000000\n",
       "Exterior1st      Exterior1st         0.000000\n",
       "Exterior2nd      Exterior2nd         0.000000\n",
       "MasVnrType        MasVnrType         0.547945\n",
       "MasVnrArea        MasVnrArea         0.547945\n",
       "ExterQual          ExterQual         0.000000\n",
       "ExterCond          ExterCond         0.000000\n",
       "Foundation        Foundation         0.000000\n",
       "...                      ...              ...\n",
       "BedroomAbvGr    BedroomAbvGr         0.000000\n",
       "KitchenAbvGr    KitchenAbvGr         0.000000\n",
       "KitchenQual      KitchenQual         0.000000\n",
       "TotRmsAbvGrd    TotRmsAbvGrd         0.000000\n",
       "Functional        Functional         0.000000\n",
       "Fireplaces        Fireplaces         0.000000\n",
       "FireplaceQu      FireplaceQu        47.260274\n",
       "GarageType        GarageType         5.547945\n",
       "GarageYrBlt      GarageYrBlt         5.547945\n",
       "GarageFinish    GarageFinish         5.547945\n",
       "GarageCars        GarageCars         0.000000\n",
       "GarageArea        GarageArea         0.000000\n",
       "GarageQual        GarageQual         5.547945\n",
       "GarageCond        GarageCond         5.547945\n",
       "PavedDrive        PavedDrive         0.000000\n",
       "WoodDeckSF        WoodDeckSF         0.000000\n",
       "OpenPorchSF      OpenPorchSF         0.000000\n",
       "EnclosedPorch  EnclosedPorch         0.000000\n",
       "3SsnPorch          3SsnPorch         0.000000\n",
       "ScreenPorch      ScreenPorch         0.000000\n",
       "PoolArea            PoolArea         0.000000\n",
       "PoolQC                PoolQC        99.520548\n",
       "Fence                  Fence        80.753425\n",
       "MiscFeature      MiscFeature        96.301370\n",
       "MiscVal              MiscVal         0.000000\n",
       "MoSold                MoSold         0.000000\n",
       "YrSold                YrSold         0.000000\n",
       "SaleType            SaleType         0.000000\n",
       "SaleCondition  SaleCondition         0.000000\n",
       "SalePrice          SalePrice         0.000000\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'column_name': df.columns,\n",
    "                               'percent_missing': percent_missing})\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Id','MSZoning','Street','LotShape','LotConfig','LandSlope','Neighborhood','Condition1','Condition2','BldgType',\n",
    "         'HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','ExterQual','ExterCond','Foundation',\n",
    "         'BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2',\n",
    "         'Alley','LotArea','LandContour','Utilities','PoolQC','Fence','MiscFeature','SaleType',\n",
    "         'SaleCondition','PavedDrive','GarageCond','GarageQual','SalePrice']\n",
    "        , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BsmtUnfSF\t...\tGarageArea\n",
    "# X[['TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical',\n",
    "#        '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n",
    "#        'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n",
    "#        'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces',\n",
    "#        'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish',\n",
    "#        'GarageCars']]\n",
    "X1 = X.drop(['Heating','HeatingQC','CentralAir','Electrical','KitchenQual','Functional','FireplaceQu','GarageType',\n",
    "             'GarageFinish']\n",
    "               , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning      ...       YrSold  SaleType SaleCondition\n",
       "0  1461          20       RH      ...         2010        WD        Normal\n",
       "1  1462          20       RL      ...         2010        WD        Normal\n",
       "2  1463          60       RL      ...         2010        WD        Normal\n",
       "3  1464          60       RL      ...         2010        WD        Normal\n",
       "4  1465         120       RL      ...         2010        WD        Normal\n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df2 = pd.read_csv(\"../input/test.csv\")\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = df2.drop(['Id','MSZoning','Street','LotShape','LotConfig','LandSlope','Neighborhood','Condition1','Condition2','BldgType',\n",
    "         'HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','ExterQual','ExterCond','Foundation',\n",
    "         'BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2',\n",
    "         'Alley','LotArea','LandContour','Utilities','PoolQC','Fence','MiscFeature','SaleType',\n",
    "         'SaleCondition','PavedDrive','GarageCond','GarageQual']\n",
    "        , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = X2.drop(['Heating','HeatingQC','CentralAir','Electrical','KitchenQual','Functional','FireplaceQu','GarageType',\n",
    "             'GarageFinish']\n",
    "               , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = X2.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>Id</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>MSSubClass</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>MSZoning</td>\n",
       "      <td>0.274160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>15.558602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>Street</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>Alley</td>\n",
       "      <td>92.666210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape</th>\n",
       "      <td>LotShape</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour</th>\n",
       "      <td>LandContour</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.137080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig</th>\n",
       "      <td>LotConfig</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>LandSlope</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1</th>\n",
       "      <td>Condition1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2</th>\n",
       "      <td>Condition2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <td>BldgType</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle</th>\n",
       "      <td>HouseStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>YearRemodAdd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>RoofStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>RoofMatl</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>Exterior1st</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>Exterior2nd</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>MasVnrType</td>\n",
       "      <td>1.096642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>1.028101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>ExterQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>ExterCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>Foundation</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>HalfBath</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>KitchenAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>KitchenQual</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>TotRmsAbvGrd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>Functional</td>\n",
       "      <td>0.137080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>Fireplaces</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>FireplaceQu</td>\n",
       "      <td>50.034270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>GarageType</td>\n",
       "      <td>5.209047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>GarageYrBlt</td>\n",
       "      <td>5.346127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>GarageFinish</td>\n",
       "      <td>5.346127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>GarageArea</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>GarageQual</td>\n",
       "      <td>5.346127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>GarageCond</td>\n",
       "      <td>5.346127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive</th>\n",
       "      <td>PavedDrive</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>EnclosedPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>ScreenPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>PoolArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>PoolQC</td>\n",
       "      <td>99.794380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>Fence</td>\n",
       "      <td>80.123372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>MiscFeature</td>\n",
       "      <td>96.504455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>MiscVal</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>MoSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>YrSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>SaleType</td>\n",
       "      <td>0.068540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>SaleCondition</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 column_name  percent_missing\n",
       "Id                        Id         0.000000\n",
       "MSSubClass        MSSubClass         0.000000\n",
       "MSZoning            MSZoning         0.274160\n",
       "LotFrontage      LotFrontage        15.558602\n",
       "LotArea              LotArea         0.000000\n",
       "Street                Street         0.000000\n",
       "Alley                  Alley        92.666210\n",
       "LotShape            LotShape         0.000000\n",
       "LandContour      LandContour         0.000000\n",
       "Utilities          Utilities         0.137080\n",
       "LotConfig          LotConfig         0.000000\n",
       "LandSlope          LandSlope         0.000000\n",
       "Neighborhood    Neighborhood         0.000000\n",
       "Condition1        Condition1         0.000000\n",
       "Condition2        Condition2         0.000000\n",
       "BldgType            BldgType         0.000000\n",
       "HouseStyle        HouseStyle         0.000000\n",
       "OverallQual      OverallQual         0.000000\n",
       "OverallCond      OverallCond         0.000000\n",
       "YearBuilt          YearBuilt         0.000000\n",
       "YearRemodAdd    YearRemodAdd         0.000000\n",
       "RoofStyle          RoofStyle         0.000000\n",
       "RoofMatl            RoofMatl         0.000000\n",
       "Exterior1st      Exterior1st         0.068540\n",
       "Exterior2nd      Exterior2nd         0.068540\n",
       "MasVnrType        MasVnrType         1.096642\n",
       "MasVnrArea        MasVnrArea         1.028101\n",
       "ExterQual          ExterQual         0.000000\n",
       "ExterCond          ExterCond         0.000000\n",
       "Foundation        Foundation         0.000000\n",
       "...                      ...              ...\n",
       "HalfBath            HalfBath         0.000000\n",
       "BedroomAbvGr    BedroomAbvGr         0.000000\n",
       "KitchenAbvGr    KitchenAbvGr         0.000000\n",
       "KitchenQual      KitchenQual         0.068540\n",
       "TotRmsAbvGrd    TotRmsAbvGrd         0.000000\n",
       "Functional        Functional         0.137080\n",
       "Fireplaces        Fireplaces         0.000000\n",
       "FireplaceQu      FireplaceQu        50.034270\n",
       "GarageType        GarageType         5.209047\n",
       "GarageYrBlt      GarageYrBlt         5.346127\n",
       "GarageFinish    GarageFinish         5.346127\n",
       "GarageCars        GarageCars         0.068540\n",
       "GarageArea        GarageArea         0.068540\n",
       "GarageQual        GarageQual         5.346127\n",
       "GarageCond        GarageCond         5.346127\n",
       "PavedDrive        PavedDrive         0.000000\n",
       "WoodDeckSF        WoodDeckSF         0.000000\n",
       "OpenPorchSF      OpenPorchSF         0.000000\n",
       "EnclosedPorch  EnclosedPorch         0.000000\n",
       "3SsnPorch          3SsnPorch         0.000000\n",
       "ScreenPorch      ScreenPorch         0.000000\n",
       "PoolArea            PoolArea         0.000000\n",
       "PoolQC                PoolQC        99.794380\n",
       "Fence                  Fence        80.123372\n",
       "MiscFeature      MiscFeature        96.504455\n",
       "MiscVal              MiscVal         0.000000\n",
       "MoSold                MoSold         0.000000\n",
       "YrSold                YrSold         0.000000\n",
       "SaleType            SaleType         0.068540\n",
       "SaleCondition  SaleCondition         0.000000\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = df2.isnull().sum() * 100 / len(df2)\n",
    "missing_values = pd.DataFrame({'column_name': df2.columns,\n",
    "                               'percent_missing': percent_missing})\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56eb824240>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "corr=df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Machine Learning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1944x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thanks to: https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "names = [\"Nearest Neighbors\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "X = X1.values\n",
    "pca = PCA(n_components=2,svd_solver='full')\n",
    "X = pca.fit_transform(X)\n",
    "y = df['SalePrice']\n",
    "\n",
    "rng = np.random.RandomState(2)\n",
    "\n",
    "datasets = [df]\n",
    "\n",
    "figure = plt.figure(figsize=(27, 9))\n",
    "i = 1\n",
    "# iterate over datasets\n",
    "for ds_cnt, ds in enumerate(datasets):\n",
    "    X = StandardScaler().fit_transform(X)\n",
    "    X_train, X_test, y_train, y_test = \\\n",
    "        train_test_split(X, y, test_size=.3, random_state=42)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    # just plot the dataset first\n",
    "    cm = plt.cm.RdBu\n",
    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
    "    if ds_cnt == 0:\n",
    "        ax.set_title(\"Input data\")\n",
    "    # Plot the training points\n",
    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "               edgecolors='k')\n",
    "    # Plot the testing points\n",
    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
    "               edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    i += 1\n",
    "\n",
    "    # iterate over classifiers\n",
    "    for name, clf in zip(names, classifiers):\n",
    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
    "        clf.fit(X_train, y_train)\n",
    "        score = clf.score(X_test, y_test)\n",
    "\n",
    "        # Plot the decision boundary. For that, we will assign a color to each\n",
    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "        if hasattr(clf, \"decision_function\"):\n",
    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "        else:\n",
    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "\n",
    "        # Put the result into a color plot\n",
    "        Z = Z.reshape(xx.shape)\n",
    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
    "\n",
    "        # Plot the training points\n",
    "        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "                   edgecolors='k')\n",
    "        # Plot the testing points\n",
    "        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
    "                   edgecolors='k', alpha=0.6)\n",
    "\n",
    "        ax.set_xlim(xx.min(), xx.max())\n",
    "        ax.set_ylim(yy.min(), yy.max())\n",
    "        ax.set_xticks(())\n",
    "        ax.set_yticks(())\n",
    "        if ds_cnt == 0:\n",
    "            ax.set_title(name)\n",
    "        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n",
    "                size=15, horizontalalignment='right')\n",
    "        i += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_Train = X1.values\n",
    "Y_Train = df['SalePrice']\n",
    "\n",
    "X_Train = StandardScaler().fit_transform(X_Train)\n",
    "\n",
    "X_Test = X2.values\n",
    "X_Test = StandardScaler().fit_transform(X_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing :\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from itertools import product\n",
    "\n",
    "# Classifiers :\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.426027397260274"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedmodel = LogisticRegression().fit(X_Train,Y_Train)\n",
    "predictions =trainedmodel.predict(X_Test)\n",
    "trainedmodel.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993150684931507"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "trainedforest.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"Id\": df2[\"Id\"],\n",
    "        \"SalePrice\": predictionforest\n",
    "    })\n",
    "submission.to_csv('sample_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6801369863013699"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "trainedsvm.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993150684931507"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "trainedtree.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Discriminant Anaylsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.541095890410959"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedlda = LinearDiscriminantAnalysis().fit(X_Train, Y_Train)\n",
    "predictionlda = trainedlda.predict(X_Test)\n",
    "trainedlda.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.584931506849315"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainednb = GaussianNB().fit(X_Train, Y_Train)\n",
    "predictionnb = trainednb.predict(X_Test)\n",
    "trainednb.score(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 3600x3960 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "model = XGBClassifier()\n",
    "\n",
    "# Train\n",
    "model.fit(X_Train, Y_Train)\n",
    "\n",
    "plot_tree(model)\n",
    "plt.figure(figsize = (50,55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "0.36164383561643837\n",
      "SVM\n",
      "0.02671232876712329\n",
      "Random Forest\n",
      "0.9993150684931507\n",
      "Logistic Regression\n",
      "0.028082191780821917\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2,svd_solver='full')\n",
    "\n",
    "X_Train = X1.values\n",
    "Y_Train = df['SalePrice']\n",
    "\n",
    "X_Train = StandardScaler().fit_transform(X_Train)\n",
    "X_Train1 = pca.fit_transform(X_Train)\n",
    "\n",
    "X_Test = X2.values\n",
    "X_Test = StandardScaler().fit_transform(X_Test)\n",
    "X_Test1 = pca.fit_transform(X_Train)\n",
    "\n",
    "trainednb = GaussianNB().fit(X_Train1, Y_Train)\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train1, Y_Train)\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train1,Y_Train)\n",
    "trainedmodel = LogisticRegression().fit(X_Train1,Y_Train)\n",
    "\n",
    "print('Naive Bayes')\n",
    "predictionnb = trainednb.predict(X_Test1)\n",
    "print(trainednb.score(X_Train1, Y_Train))\n",
    "\n",
    "print('SVM')\n",
    "predictionsvm = trainedsvm.predict(X_Test1)\n",
    "print(trainedsvm.score(X_Train1, Y_Train))\n",
    "\n",
    "print('Random Forest')\n",
    "predictionforest = trainedforest.predict(X_Test1)\n",
    "print(trainedforest.score(X_Train1, Y_Train))\n",
    "\n",
    "print('Logistic Regression')\n",
    "predictions =trainedmodel.predict(X_Test1)\n",
    "print(trainedmodel.score(X_Train1, Y_Train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thanks to: https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html\n",
    "\n",
    "x_min, x_max = X_Train1[:, 0].min() - 1, X_Train1[:, 0].max() + 1\n",
    "y_min, y_max = X_Train1[:, 1].min() - 1, X_Train1[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n",
    "\n",
    "for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
    "                        [trainednb, trainedsvm, trainedforest, trainedmodel],\n",
    "                        ['Naive Bayes Classifier', 'SVM',\n",
    "                         'Random Forest', 'Logistic Regression']):\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    \n",
    "\n",
    "    axarr[idx[0], idx[1]].contourf(xx, yy, Z,cmap=plt.cm.coolwarm, alpha=0.4)\n",
    "    axarr[idx[0], idx[1]].scatter(X_Train1[:, 0], X_Train1[:, 1], c=Y_Train,\n",
    "                                  s=20, edgecolor='k')\n",
    "    axarr[idx[0], idx[1]].set_title(tt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****Deep Learning****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "Y_Train = to_categorical(Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Training...\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:26: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1314 samples, validate on 146 samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.utils import np_utils\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Dense, Dropout, BatchNormalization, Activation\n",
    "\n",
    "input_dim = X_Train.shape[1]\n",
    "nb_classes = Y_Train.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(512, input_dim=input_dim))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Dense(256))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Dense(nb_classes))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "print(\"Training...\")\n",
    "model.fit(X_Train, Y_Train, nb_epoch=3, batch_size=16, validation_split=0.1, verbose=80)\n",
    "\n",
    "preds = model.predict_classes(X_Test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1460/1460 [==============================] - 37s 26ms/step\n",
      "acc: 32.60%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_Train, Y_Train)\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
